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Material and product life cycles are based on complex value chains of technology-specific elements.
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A completely new approach of power equipment modeling – modeling based on Complex-Valued Neural Networks (CVNN) – gives an opportunity to build a high-quality models which are able to track dynamics of grid devices.
On account of signal decay, low b-value DW-MRI cannot be qualitative in nature but must be quantitatively based on complex calculations of ADC values [ 10].
This includes values for parameters based on complex data types (which are decomposed in a set of individual parameters) and domain dependencies between different parameters.
While it is difficult to assign crisp classification values to measurands based on complex overlapping sensory signals, fuzzy logic offers a possibility to mathematically model such systems.
The method, which is based on complex residue theory, is approximate but nevertheless very accurate for most parameter values.
On account of this signal decay, low b-value DW-MRI becomes less qualitative and more quantitative, since it must be based on complex ADC calculations.
Another important approach is based on complex analysis.
The proposed model is based on a complex valued (CV) neural network (CVNN) structure, using polar CV basis functions architecture.
Changes in medicine are part of the complex digital economy based on creating value from analysis of behavioral data acquired by the tracking of daily digital activities.
We exploit the Gene Ontology (GO) to evaluate the function enrichment of our conserved protein complexes based on p-values [ 22].
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